Apparatus and method for analyzing model quality in a process control environment

Abstract

A method includes identifying a signal and a disturbance associated with a model. The signal and disturbance are identified using historical data associated with one or more process variables. The method also includes decomposing the signal and the disturbance at a plurality of resolution levels. The method further includes extracting a plurality of data segments from the signal using the decomposed signal and the decomposed disturbance. In addition, the method includes determining a quality of the model using the extracted data segments and at least a portion of the historical data.

1. A method, comprising:
 identifying a signal and a disturbance associated with a model, the signal and disturbance identified using historical data associated with one or more process variables; 
 decomposing the signal and the disturbance at a plurality of resolution levels; 
 extracting a plurality of data segments from the signal using the decomposed signal and the decomposed disturbance; 
 determining a quality of the model using the extracted data segments and at least a portion of the historical data; and 
 storing the quality of the model. 
 
     
     
       2. The method of  claim 1 , wherein:
 the model is associated with one or more controlled variables and one or more manipulated variables, the one or more controlled variables at least partially controllable via the one or more manipulated variables; and 
 the historical data associated with the one or more process variables comprises at least one of:
 one or more measurements associated with the one or more controlled variables; and 
 one or more measurements associated with the one or more manipulated variables. 
 
 
     
     
       3. The method of  claim 2 , wherein:
 the model comprises at least one submodel, each submodel associated with one controlled variable and one manipulated variable; 
 the signal comprises a signal for each submodel, the signal for a particular submodel comprising a prediction associated with that submodel; and 
 the disturbance comprises a disturbance for each submodel, the disturbance for a particular submodel comprising a sum of predictions associated with other submodels. 
 
     
     
       4. The method of  claim 1 , wherein extracting the plurality of data segments comprises:
 identifying a plurality of points associated with the decomposed signal; 
 selecting at least some of the points; and 
 extracting data segments associated with the selected points. 
 
     
     
       5. The method of  claim 4 , wherein:
 the points comprise local maximum and local minimum values; and 
 each selected point is associated with at least one of: a signal-to-noise ratio above a first threshold and a noise-to-signal ratio below a second threshold. 
 
     
     
       6. The method of  claim 1 , wherein determining the quality of the model comprises:
 determining a first overall predictability index for the model; and 
 determining the quality of the model based on the first overall predictability index. 
 
     
     
       7. The method of  claim 6 , wherein determining the first overall predictability index comprises:
 determining a predictability for each extracted data segment; 
 determining a predictability index for each of at least one resolution level, the predictability index for a particular resolution level based on the predictabilities for the extracted data segments associated with that resolution level; and 
 determining the first overall predictability index based on the predictability indexes for at least one of the resolution levels. 
 
     
     
       8. The method of  claim 7 , further comprising determining a second overall predictability index for the model, the quality of the model based on the first and second overall predictability indexes, the second overall predictability index determined by:
 determining a gain multiplier; 
 determining a model prediction using the gain multiplier; and 
 determining the second overall predictability index using the model prediction determined using the gain multiplier. 
 
     
     
       9. The method of  claim 7 , wherein:
 the predictability index for the particular resolution level is based on a weighted sum of the predictabilities for the extracted data segments associated with that resolution level; and 
 the first overall predictability index is based on a weighted sum of the predictability indexes for at least two of the resolution levels. 
 
     
     
       10. The method of  claim 1 , further comprising:
 comparing the quality of the model to a threshold value to determine if the model has an acceptable quality. 
 
     
     
       11. The method of  claim 10 , further comprising:
 adjusting or replacing the model if the model does not have the acceptable quality. 
 
     
     
       12. The method of  claim 1 , wherein decomposing the signal and the disturbance comprises decomposing the signal and separately decomposing the disturbance at the plurality of resolution levels. 
     
     
       13. An apparatus, comprising:
 at least one memory capable of storing historical data associated with one or more process variables; and 
 at least one processor capable of:
 identifying a signal and a disturbance associated with a model using the historical data; 
 decomposing the signal and the disturbance at a plurality of resolution levels; 
 extracting a plurality of data segments from the signal using the decomposed signal and the decomposed disturbance; and 
 determining a quality of the model using the extracted data segments and at least a portion of the historical data. 
 
 
     
     
       14. The apparatus of  claim 13 , wherein:
 the model comprises at least one submodel, each submodel associated with one controlled variable and one manipulated variable, the controlled variable at least partially controllable via the manipulated variable; 
 the signal comprises a signal for each submodel, the signal for a particular submodel comprising a prediction associated with that submodel; and 
 the disturbance comprises a disturbance for each submodel, the disturbance for a particular submodel comprising a sum of predictions associated with other submodels. 
 
     
     
       15. The apparatus of  claim 13 , wherein the at least one processor is capable of extracting the plurality of data segments by:
 identifying a plurality of points associated with the decomposed signal; 
 selecting at least some of the points; and 
 extracting data segments associated with the selected points. 
 
     
     
       16. The apparatus of  claim 13 , wherein the at least one processor is capable of determining the quality of the model by:
 determining a first overall predictability index for the model; and 
 determining the quality of the model based on the first overall predictability index. 
 
     
     
       17. The apparatus of  claim 16 , wherein the at least one processor is capable of determining the first overall predictability index by:
 determining a predictability for each extracted data segment; 
 determining a predictability index for each of at least one resolution level, the predictability index for a particular resolution level based on the predictabilities for the extracted data segments associated with that resolution level; and 
 determining the first overall predictability index based on the predictability indexes for at least one of the resolution levels. 
 
     
     
       18. The apparatus of  claim 17 , wherein the at least one processor is further capable of determining a second overall predictability index for the model, the quality of the model based on the first and second overall predictability indexes, the second overall predictability index determined by:
 determining a gain multiplier; 
 determining a model prediction using the gain multiplier; and 
 determining the second overall predictability index using the model prediction determined using the gain multiplier. 
 
     
     
       19. The apparatus of  claim 17 , wherein:
 the predictability index for the particular resolution level is based on a weighted sum of the predictabilities for the extracted data segments associated with that resolution level; and 
 the first overall predictability index is based on a weighted sum of the predictability indexes for at least two of the resolution levels. 
 
     
     
       20. A computer program embodied on a computer readable medium, the computer program comprising computer readable program code for:
 identifying a signal and a disturbance associated with a model, the signal and disturbance identified using historical data associated with one or more process variables; 
 decomposing the signal and the disturbance at a plurality of resolution levels; 
 extracting a plurality of data segments from the signal using the decomposed signal and the decomposed disturbance; and 
 determining a quality of the model using the extracted data segments and at least a portion of the historical data. 
 
     
     
       21. The computer program of  claim 20 , wherein the computer readable program code for determining the quality of the model comprises computer readable program code for:
 determining a first overall predictability index for the model; and 
 determining the quality of the model based on the first overall predictability index. 
 
     
     
       22. The computer program of  claim 21 , wherein the computer readable program code for determining the first overall predictability index comprises computer readable program code for:
 determining a predictability for each extracted data segment; 
 determining a predictability index for each of at least one resolution level, the predictability index for a particular resolution level based on a weighted sum of the predictabilities for the extracted data segments associated with that resolution level; and 
 determining the first overall predictability index based on a weighted sum of the predictability indexes for at least one of the resolution levels. 
 
     
     
       23. The computer program of  claim 22 , wherein the computer program further comprises computer readable program code for determining a second overall predictability index, the quality of the model based on the first and second overall predictability indexes, the second overall predictability index determined by:
 determining a gain multiplier; 
 determining a model prediction using the gain multiplier; and 
 determining the second overall predictability index using the model prediction determined using the gain multiplier.